All of the above mentioned methods for solving (REF ) can be cast into the family of stochastic first-order algorithms. Although the iteration in stochastic first-order algorithms is computationally cheap and these methods perform well for certain problems, there are plenty of practical experiences and evidences of their convergence difficulties and instability with respect to the choice of parameters. In order to overcome these drawbacks, the stochastic proximal point methods have recently attracted much attention for stochastic optimization problems (without expectation constraints) [1]}, [2]}, [3]}. Motivated by the success of these methods, the aim of this paper is to develop an efficient stochastic proximal point method for solving (REF ). To the best of our knowledge, this is still limited in the literature.
